In wireless digital communications, the radio environment presents many difficulties that impede successful communications. One difficulty is that the transmitted signal may produce multiple reflections which arrive at the receiver with different amplitudes and different phases. The interaction of these reflections, or images, produces variations of the received signal strength at the receiver known as flat fading. If there are a large number of images, flat fading gives rise to a Rayleigh distribution.
A second problem which impedes wireless digital communications is known as time dispersion. Time dispersion occurs when the signal images arrive at the receiver delayed in time with respect to one another. If the time delays are a significant portion of a symbol period, then intersymbol interference (ISI) is produced.
The deleterious effects of Rayleigh fading can be overcome by using diversity at the receiver. One known method of diversity is to use a receiver having two or more mutually separated antennas, for instance as described in Mobile Communications Design Fundamentals by William C. Y. Lee, W. Sams and Co., Indiana, USA. In section 3.5.1 of this book several examples are given describing how signals from two receiver antennas can be combined to counteract fading.
Time dispersion can be mitigated by digitally processing the received signals with the aid of an equalizer. Common forms of equalization are provided by linear equalizers, decision-feedback equalizers (DFE), and maximum likelihood sequence estimation (MLSE) equalizers. A linear equalizer attempts to "undo" the effects of the transmission channel by filtering the received signal. A decision feedback equalizer exploits previous signal detections to cancel out the intersymbol interference caused by echoes from these previous signals. Finally, an MLSE equalizer hypothesizes various transmitted signal sequences and applies a model of the disturbed transmission channel to determine which hypothesis best fits the received data. These equalization techniques are well-known to one of ordinary skill in the art and can be found in standard textbooks such as J. G. Proakis, Digital Communications, 2nd. Edition, New York: McGraw Hill, 1989.
Of these three equalization methods, MLSE equalization offers the best overall performance. In an MLSE equalizer, all possible signal sequences are hypothesized. For each hypothetical signal sequence, the received signal samples are predicted using a model of the disturbed transmission channel. The difference between the hypothesized received signal and the actual received signal, referred to as the prediction error, gives an indication of how accurate a particular hypothesis is. The squared magnitude of the prediction error is then used as a metric to evaluate each particular hypothesis. The metric is accumulated for different individual hypotheses for use in determining which hypothetical signal sequences are better. This process may, for example, be efficiently realized using the Viterbi algorithm which is a form of dynamic programming.
It is known that the diversity combining process and the equalization process may be combined in some way. Recent research has shown that for MLSE equalization, diversity combining may be done within the equalizer. (See, for example: W. H. Sheen, et al., "MLSE equalization and decoding for multipath fading channels", IEEE Trans. Communications, vol. 39, pp. 1455-1464, Oct., 1991; or Q. Liu, et al., "An adaptive maximum-likelihood sequence estimation receiver with dual diversity combining/selection," Intl. Symp. on Personal, Indoor and Mobile Radio Communications, Boston, Mass., pp. 245-249 Oct. 19-21, 1992; and Q. Liu, et al. "A unified MLSE detection technique for TDMA digital cellular radio", 43rd IEEE Vehicular Technology Conference, Secaucus, N.J., pp. 265-268, May 18-20, 1993.) In the above mentioned research, diversity combining is performed by adding together the magnitude squared prediction errors from different diversity channels when performing metrics.
Further improvement is obtained by scaling the prediction errors from different diversity branches. A detailed description of such an MSLE equalizer is given in U.S. Pat. No. 5,191,598 to Thomas Backstrom, et al. A drawback to the techniques heretofore described is that the previous implementation of a-diversity combining MLSE equalizer involves computing many squared prediction error terms. This can be costly in terms of hardware or software complexity. Thus, there is a need to reduce the complexity of the MLSE equalizer which includes diversity combining.